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Sitorus A, Lapcharoensuk R. Exploring Deep Learning to Predict Coconut Milk Adulteration Using FT-NIR and Micro-NIR Spectroscopy. SENSORS (BASEL, SWITZERLAND) 2024; 24:2362. [PMID: 38610572 PMCID: PMC11014270 DOI: 10.3390/s24072362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 04/04/2024] [Accepted: 04/06/2024] [Indexed: 04/14/2024]
Abstract
Accurately identifying adulterants in agriculture and food products is associated with preventing food safety and commercial fraud activities. However, a rapid, accurate, and robust prediction model for adulteration detection is hard to achieve in practice. Therefore, this study aimed to explore deep-learning algorithms as an approach to accurately identify the level of adulterated coconut milk using two types of NIR spectrophotometer, including benchtop FT-NIR and portable Micro-NIR. Coconut milk adulteration samples came from deliberate adulteration with corn flour and tapioca starch in the 1 to 50% range. A total of four types of deep-learning algorithm architecture that were self-modified to a one-dimensional framework were developed and tested to the NIR dataset, including simple CNN, S-AlexNET, ResNET, and GoogleNET. The results confirmed the feasibility of deep-learning algorithms for predicting the degree of coconut milk adulteration by corn flour and tapioca starch using NIR spectra with reliable performance (R2 of 0.886-0.999, RMSE of 0.370-6.108%, and Bias of -0.176-1.481). Furthermore, the ratio of percent deviation (RPD) of all algorithms with all types of NIR spectrophotometers indicates an excellent capability for quantitative predictions for any application (RPD > 8.1) except for case predicting tapioca starch, using FT-NIR by ResNET (RPD < 3.0). This study demonstrated the feasibility of using deep-learning algorithms and NIR spectral data as a rapid, accurate, robust, and non-destructive way to evaluate coconut milk adulterants. Last but not least, Micro-NIR is more promising than FT-NIR in predicting coconut milk adulteration from solid adulterants, and it is portable for in situ measurements in the future.
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Affiliation(s)
| | - Ravipat Lapcharoensuk
- Department of Agricultural Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
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Li P, Shen T, Li L, Wang Y. Optimization of the selection of suitable harvesting periods for medicinal plants: taking Dendrobium officinale as an example. PLANT METHODS 2024; 20:43. [PMID: 38493140 PMCID: PMC10943765 DOI: 10.1186/s13007-024-01172-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 03/09/2024] [Indexed: 03/18/2024]
Abstract
BACKGROUND Dendrobium officinale is a medicinal plant with high commercial value. The Dendrobium officinale market in Yunnan is affected by the standardization of medicinal material quality control and the increase in market demand, mainly due to the inappropriate harvest time, which puts it under increasing resource pressure. In this study, considering the high polysaccharide content of Dendrobium leaves and its contribution to today's medical industry, (Fourier Transform Infrared Spectrometer) FTIR combined with chemometrics was used to combine the yields of both stem and leaf parts of Dendrobium officinale to identify the different harvesting periods and to predict the dry matter content for the selection of the optimal harvesting period. RESULTS The Three-dimensional correlation spectroscopy (3DCOS) images of Dendrobium stems to build a (Split-Attention Networks) ResNet model can identify different harvesting periods 100%, which is 90% faster than (Support Vector Machine) SVM, and provides a scientific basis for modeling a large number of samples. The (Partial Least Squares Regression) PLSR model based on MSC preprocessing can predict the dry matter content of Dendrobium stems with Factor = 7, RMSE = 0.47, R2 = 0.99, RPD = 8.79; the PLSR model based on SG preprocessing can predict the dry matter content of Dendrobium leaves with Factor = 9, RMSE = 0.2, R2 = 0.99, RPD = 9.55. CONCLUSIONS These results show that the ResNet model possesses a fast and accurate recognition ability, and at the same time can provide a scientific basis for the processing of a large number of sample data; the PLSR model with MSC and SG preprocessing can predict the dry matter content of Dendrobium stems and leaves, respectively; The suitable harvesting period for D. officinale is from November to April of the following year, with the best harvesting period being December. During this period, it is necessary to ensure sufficient water supply between 7:00 and 10:00 every day and to provide a certain degree of light blocking between 14:00 and 17:00.
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Affiliation(s)
- Peiyuan Li
- College of Biology and Environmental Sciences of Hunan Province, Jishou University, Jishou, 416000, China
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, China
| | - Tao Shen
- College of Chemistry, Biological and Environment, Yuxi Normal University, Yuxi, 653100, Yunnan, China
| | - Li Li
- College of Biology and Environmental Sciences of Hunan Province, Jishou University, Jishou, 416000, China.
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, China.
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Shi S, Tang Z, Ma Y, Cao C, Jiang Y. Application of spectroscopic techniques combined with chemometrics to the authenticity and quality attributes of rice. Crit Rev Food Sci Nutr 2023:1-23. [PMID: 38010116 DOI: 10.1080/10408398.2023.2284246] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Rice is a staple food for two-thirds of the world's population and is grown in over a hundred countries around the world. Due to its large scale, it is vulnerable to adulteration. In addition, the quality attribute of rice is an important factor affecting the circulation and price, which is also paid more and more attention. The combination of spectroscopy and chemometrics enables rapid detection of authenticity and quality attributes in rice. This article described the application of seven spectroscopic techniques combined with chemometrics to the rice industry. For a long time, near-infrared spectroscopy and linear chemometric methods (e.g., PLSR and PLS-DA) have been widely used in the rice industry. Although some studies have achieved good accuracy, with models in many studies having greater than 90% accuracy. However, higher accuracy and stability were more likely to be obtained using multiple spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. Future research should develop larger rice databases to include more rice varieties and larger amounts of rice depending on the type of rice, and then combine various spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. This article provided a reference for a more efficient and accurate determination of rice quality and authenticity.
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Affiliation(s)
- Shijie Shi
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Zihan Tang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Yingying Ma
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Cougui Cao
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
- Shuangshui Shuanglü Institute, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Yang Jiang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
- Shuangshui Shuanglü Institute, Huazhong Agricultural University, Wuhan, Hubei, China
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Chai Y, Yu Y, Zhu H, Li Z, Dong H, Yang H. Identification of common buckwheat ( Fagopyrum esculentum Moench) adulterated in Tartary buckwheat ( Fagopyrum tataricum (L.) Gaertn) flour based on near-infrared spectroscopy and chemometrics. Curr Res Food Sci 2023; 7:100573. [PMID: 37650007 PMCID: PMC10463190 DOI: 10.1016/j.crfs.2023.100573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 08/17/2023] [Accepted: 08/20/2023] [Indexed: 09/01/2023] Open
Abstract
Near-infrared spectroscopy (NIRS) presents great potential in the identification of food adulteration due to its advantages of nondestructive, simple, and easy to operate. In this paper, a method based on NIRS and chemometrics was proposed to predict the content of common buckwheat (Fagopyrum esculentum Moench) flour in Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) flour. Partial least squares regression (PLSR) and support vector regression (SVR) models were used to analyze the spectrum data of adulterated samples and predict the adulteration level. Various preprocessing methods, parameter-optimization methods, and competitive adaptive reweighted sampling (CARS) wavelength-selection methods were used to optimize the model prediction accuracy. The results of PLSR and SVR modeling for predicting of Tartary buckwheat adulteration content were satisfactory, and the correlation coefficients of the optimum identification models were above 0.99. In conclusion, the combinations of NIRS and chemometrics indicated excellent predictive performance and applicability to analyze the adulteration of common buckwheat flour in Tartary buckwheat flour. This work provides a promising method to identify the adulteration of Tartary buckwheat flour and results obtained can give theoretical and data support for adulteration identification of agro-products.
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Affiliation(s)
- Yinghui Chai
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang, 212100, China
| | - Yue Yu
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang, 212100, China
| | - Hui Zhu
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang, 212100, China
| | - Zhanming Li
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang, 212100, China
- Liyang Tianmu Lake Agricultural Development Co., Ltd., Liyang, 213333, China
| | - Hao Dong
- College of Light Industry and Food Sciences, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, China
| | - Hongshun Yang
- Shaoxing Key Laboratory of Traditional Fermentation Food and Human Health, Jiangnan University (Shaoxing) Industrial Technology Research Institute, Zhejiang, 312000, China
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Wu Y, Li Z, Zhu H, Zi R, Xue F, Yu Y. Identification of Tartary Buckwheat ( Fagopyrum tataricum (L.) Gaertn) and Common Buckwheat ( Fagopyrum esculentum Moench) Using Gas Chromatography-Mass Spectroscopy-Based Untargeted Metabolomics. Foods 2023; 12:2578. [PMID: 37444316 DOI: 10.3390/foods12132578] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 06/28/2023] [Accepted: 06/29/2023] [Indexed: 07/15/2023] Open
Abstract
Tartary buckwheat has attracted more attention than common buckwheat due to its unique chemical composition and higher efficacy in the prevention of various diseases. The content of flavonoids in Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) is higher than that in common buckwheat (Fagopyrum esculentum Moench). However, the processing process of Tartary buckwheat is complex, and the cost is high, which leads to the frequent phenomenon of common buckwheat counterfeiting and adulteration in Tartary buckwheat, which seriously damages the interests of consumers and disrupts the market order. In order to explore a new and simple identification method for Tartary buckwheat and common buckwheat, this article uses metabolomics technology based on GC-MS to identify Tartary buckwheat and common buckwheat. The results show that the PLS-DA model can identify Tartary buckwheat and common buckwheat, as well as Tartary buckwheat from different regions, without an over-fitting phenomenon. It was also found that ascorbate and aldarate metabolism was the main differential metabolic pathway between Tartary buckwheat and common buckwheat, as well as the amino acids biosynthesis pathway. This study provides a new attempt for the identification of Tartary buckwheat and common buckwheat for the quality control of related agricultural products.
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Affiliation(s)
- Yuling Wu
- School of Grain Science and Technology, Jiangsu University of Food Science and Technology, Zhenjiang 212100, China
| | - Zhanming Li
- School of Grain Science and Technology, Jiangsu University of Food Science and Technology, Zhenjiang 212100, China
- National University of Singapore Suzhou Research Institute, Suzhou 215127, China
| | - Hui Zhu
- School of Grain Science and Technology, Jiangsu University of Food Science and Technology, Zhenjiang 212100, China
| | - Run Zi
- National University of Singapore Suzhou Research Institute, Suzhou 215127, China
| | - Fang Xue
- School of Grain Science and Technology, Jiangsu University of Food Science and Technology, Zhenjiang 212100, China
| | - Yue Yu
- School of Grain Science and Technology, Jiangsu University of Food Science and Technology, Zhenjiang 212100, China
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Geng L, Liu K, Zhang H. Lipid oxidation in foods and its implications on proteins. Front Nutr 2023; 10:1192199. [PMID: 37396138 PMCID: PMC10307983 DOI: 10.3389/fnut.2023.1192199] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 05/25/2023] [Indexed: 07/04/2023] Open
Abstract
Lipids in foods are sensitive to various environmental conditions. Under light or high temperatures, free radicals could be formed due to lipid oxidation, leading to the formation of unstable food system. Proteins are sensitive to free radicals, which could cause protein oxidation and aggregation. Protein aggregation significantly affects protein physicochemical characteristics and biological functions, such as digestibility, foaming characteristics, and bioavailability, further reducing the edible and storage quality of food. This review provided an overview of lipid oxidation in foods; its implications on protein oxidation; and the assessment methods of lipid oxidation, protein oxidation, and protein aggregation. Protein functions before and after aggregation in foods were compared, and a discussion for future research on lipid or protein oxidation in foods was presented.
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Affiliation(s)
- Lianxin Geng
- College of Food Science and Engineering, Henan University of Technology, Zhengzhou, China
| | - Kunlun Liu
- College of Food Science and Engineering, Henan University of Technology, Zhengzhou, China
- School of Food and Reserves Storage, Henan University of Technology, Zhengzhou, China
| | - Huiyan Zhang
- Zhengzhou Ruipu Biological Engineering Co., Ltd, Zhengzhou, China
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Xia Z, Che X, Ye L, Zhao N, Guo D, Peng Y, Lin Y, Liu X. A Synergetic Strategy for Brand Characterization of Colla Corii Asini (Ejiao) by LIBS and NIR Combined with Partial Least Squares Discriminant Analysis. Molecules 2023; 28:molecules28041778. [PMID: 36838765 PMCID: PMC9965801 DOI: 10.3390/molecules28041778] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 02/02/2023] [Accepted: 02/07/2023] [Indexed: 02/16/2023] Open
Abstract
A synergetic strategy was proposed to address the critical issue in the brand characterization of Colla corii asini (Ejiao, CCA), a precious traditional Chinese medicine (TCM). In all brands of CCA, Dong'e Ejiao (DEEJ) is an intangible cultural heritage resource. Seventy-eight CCA samples (including forty DEEJ samples and thirty-eight samples from other different manufacturers) were detected by laser-induced breakdown spectroscopy (LIBS) and near-infrared spectroscopy (NIR). Partial least squares discriminant analysis (PLS-DA) models were built first considering individual techniques separately, and then fusing LIBS and NIR data at low-level. The statistical parameters including classification accuracy, sensitivity, and specificity were calculated to evaluate the PLS-DA model performance. The results demonstrated that two individual techniques show good classification performance, especially the NIR. The PLS-DA model with single NIR spectra pretreated by the multiplicative scatter correction (MSC) method was preferred as excellent discrimination. Though individual spectroscopic data obtained good classification performance. A data fusion strategy was also attempted to merge atomic and molecular information of CCA. Compared to a single data block, data fusion models with SNV and MSC pretreatment exhibited good predictive power with no misclassification. This study may provide a novel perspective to employ a comprehensive analytical approach to brand discrimination of CCA. The synergetic strategy based on LIBS together with NIR offers atomic and molecular information of CCA, which could be exemplary for future research on the rapid discrimination of TCM.
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Affiliation(s)
- Ziyi Xia
- College of Integrated Traditional Chinese and Western Medicine, Binzhou Medical University, Yantai 264003, China
| | - Xiaoqing Che
- Shandong Runzhong Pharmaceutical Co., Ltd., Yantai 256603, China
| | - Lei Ye
- College of Integrated Traditional Chinese and Western Medicine, Binzhou Medical University, Yantai 264003, China
| | - Na Zhao
- Key Laboratory of Xinjiang Phytomedicine Resources and Utilization in Ministry of Education, School of Pharmacy, Shihezi University, Shihezi 832002, China
| | - Dongxiao Guo
- Shandong Institute of Food and Drug Inspection, Jinan 250101, China
| | - Yanfang Peng
- Pharmacy Faculty, Hubei University of Chinese Medicine, Wuhan 430065, China
| | - Yongqiang Lin
- Shandong Institute of Food and Drug Inspection, Jinan 250101, China
- Correspondence: (Y.L.); (X.L.)
| | - Xiaona Liu
- College of Integrated Traditional Chinese and Western Medicine, Binzhou Medical University, Yantai 264003, China
- Correspondence: (Y.L.); (X.L.)
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